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Seeking informative projecting directions has been an important task in utilizing sliced Wasserstein distance in applications.
Optimal transport: Old and New
C. Villani · 2008
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Learning multiple layers of features from tiny images
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An analysis of single-layer networks in unsupervised feature learning
A. Coates, A. Ng, and H. Lee · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Amortized inference in probabilistic reasoning
S. Gershman and N. Goodman · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Sliced and Radon Wasserstein barycenters of measures
N. Bonneel, J. Rabin, G. Peyré, and H. Pfister · 2015
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Structural learning with amortized inference
K.-W. Chang, S. Upadhyay, G. Kundu, and D. Roth · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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f-gan: Training generative neural samplers using variational divergence minimization
S. Nowozin, B. Cseke, and R. Tomioka · 2016
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Deep amortized inference for probabilistic programs
D. Ritchie, P. Horsfall, and N. D. Goodman · 2016
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Improved techniques for training GANs
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
J. Altschuler, J. Niles-Weed, and P. Rigollet · 2017
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Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Amortized optimization http://ruishu.io/2017/11/07/amortized-optimization/
R. Shu · 2017
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Generative modeling using the sliced Wasserstein distance
I. Deshpande, Z. Zhang, and A. G. Schwing · 2018
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Learning generative models with Sinkhorn divergences
A. Genevay, G. Peyré, and M. Cuturi · 2018
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Sliced Wasserstein auto-encoders
S. Kolouri, P. E. Pope, C. E. Martin, and G. K. Rohde · 2018
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Iterative amortized inference
J. Marino, Y. Yue, and S. Mandt · 2018
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Spectral normalization for generative adversarial networks
T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida · 2018
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Improving GANs using optimal transport
T. Salimans, H. Zhang, A. Radford, and D. Metaxas · 2018
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Amortized inference regularization
R. Shu, H. H. Bui, S. Zhao, M. J. Kochenderfer, and S. Ermon · 2018
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Projection robust Wasserstein distance and Riemannian optimization
T. Lin, C. Fan, N. Ho, M. Cuturi, and M. Jordan · 2020
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Fixed-support Wasserstein barycenters: Computational hardness and fast algorithm
T. Lin, N. Ho, X. Chen, M. Cuturi, and M. I. Jordan · 2020
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Statistical and topological properties of sliced probability divergences
K. Nadjahi, A. Durmus, L. Chizat, S. Kolouri, S. Shahrampour, and U. Simsekli · 2020
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Computational optimal transport, 2020
G. Peyré and M. Cuturi · 2020
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Meta-amortized variational inference and learning
M. Wu, K. Choi, N. Goodman, and S. Ermon · 2020
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Sliced iterative normalizing flows
B. Dai and U. Seljak · 2021
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Max-sliced Wasserstein distance and its use for GANs
I. Deshpande, Y.-T. Hu, R. Sun, A. Pyrros, N. Siddiqui, S. Koyejo, Z. Zhao, D. Forsyth, and A. G. Schwing · 2019
Cited alongside, same era.
Generalized sliced Wasserstein distances
S. Kolouri, K. Nadjahi, U. Simsekli, R. Badeau, and G. Rohde · 2019
Cited alongside, same era.
On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
T. Lin, N. Ho, and M. Jordan · 2019
Cited alongside, same era.
On the efficiency of the Sinkhorn and Greenkhorn algorithms and their acceleration for optimal transport
T. Lin, N. Ho, and M. I. Jordan · 2019
Cited alongside, same era.
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
G. Mena and J. Weed · 2019
Cited alongside, same era.
Asymptotic guarantees for learning generative models with the sliced-Wasserstein distance
K. Nadjahi, A. Durmus, U. Simsekli, and R. Badeau · 2019
Cited alongside, same era.
Minibatch optimal transport distances; analysis and applications
K. Fatras, Y. Zine, S. Majewski, R. Flamary, R. Gribonval, and N. Courty · 2021
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Sliced mutual information: A scalable measure of statistical dependence
Z. Goldfeld and K. Greenewald · 2021
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A Riemannian block coordinate descent method for computing the projection robust Wasserstein distance
M. Huang, S. Ma, and L. Lai · 2021
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A riemannian block coordinate descent method for computing the projection robust wasserstein distance
M. Huang, S. Ma, and L. Lai · 2021
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Run-sort-rerun: Escaping batch size limitations in sliced Wasserstein generative models
J. Lezama, W. Chen, and Q. Qiu · 2021
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Fast approximation of the sliced-Wasserstein distance using concentration of random projections
K. Nadjahi, A. Durmus, P. E. Jacob, R. Badeau, and U. Simsekli · 2021
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Distributional sliced-Wasserstein and applications to generative modeling
K. Nguyen, N. Ho, T. Pham, and H. Bui · 2021
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Improving relational regularized autoencoders with spherical sliced fused Gromov-Wasserstein
K. Nguyen, S. Nguyen, N. Ho, T. Pham, and H. Bui · 2021
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Wasserstein GANs work because they fail (to approximate the Wasserstein distance)
J. Stanczuk, C. Etmann, L. M. Kreusser, and C.-B. Schönlieb · 2021
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Tutorial on amortized optimization for learning to optimize over continuous domains
B. Amos · 2022
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Augmented sliced Wasserstein distances
X. Chen, Y. Yang, and Y. Li · 2022
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On transportation of mini-batches: A hierarchical approach
K. Nguyen, D. Nguyen, Q. Nguyen, T. Pham, H. Bui, D. Phung, T. Le, and N. Ho · 2022
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Improving mini-batch optimal transport via partial transportation
K. Nguyen, D. Nguyen, T. Pham, and N. Ho · 2022
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